age returns survived vehicles
age(x, type = "weibull", a = 14.46, b = 4.79, agemax, verbose = FALSE)Numeric; numerical vector of sales or registrations for each year
Character; any of "gompertz", "double_logistic", "weibull" and "weibull2"
Numeric; parameter of survival equation
Numeric; parameter of survival equation
Integer; age of oldest vehicles for that category
Logical; message with average age and total numer of vehicles regions or streets.
dataframe of age distrubution of vehicles
The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:
1. If you know the distribution of the vehicles by age of use , use: my_age
2. If you know the sales of vehicles, or the registry of new vehicles,
use age to apply a survival function.
3. If you know the theoretical shape of the circulating fleet and you can use
age_ldv, age_hdv or age_moto. For instance,
you dont know the sales or registry of vehicles, but somehow you know
the shape of this curve.
4. You can use/merge/transform/dapt any of these functions.
gompertz: 1 - exp(-exp(a + b*time)), defaults PC: b = -0.137, a = 1.798, LCV: b = -0.141, a = 1.618 MCT (2006). de Gases de Efeito Estufa-Emissoes de Gases de Efeito Estufa por Fontes Moveis, no Setor Energético. Ministerio da Ciencia e Tecnologia. This curve is also used by Guo and Wang (2012, 2015) in the form: V*exp(alpha*exp(beta*E)) where V is the saturation car ownership level and E GDP per capita Huo, H., & Wang, M. (2012). Modeling future vehicle sales and stock in China. Energy Policy, 43, 17–29. doi:10.1016/j.enpol.2011.09.063 Huo, Hong, et al. "Vehicular air pollutant emissions in China: evaluation of past control policies and future perspectives." Mitigation and Adaptation Strategies for Global Change 20.5 (2015): 719-733.
double_logistic: 1/(1 + exp(a*(time + b))) + 1/(1 + exp(a*(time - b))), defaults PC: b = 21, a = 0.19, LCV: b = 15.3, a = 0.17, HGV: b = 17, a = 0.1, BUS: b = 19.1, a = 0.16 MCT (2006). de Gases de Efeito Estufa-Emissoes de Gases de Efeito Estufa por Fontes Moveis, no Setor Energético. Ministerio da Ciencia e Tecnologia.
weibull: exp(-(time/a)^b), defaults PC: b = 4.79, a = 14.46, Taxi: b = +inf, a = 5, Government and business: b = 5.33, a = 13.11 Non-operating vehicles: b = 5.08, a = 11.53 Bus: b = +inf, a = 9, non-transit bus: b = +inf, a = 5.5 Heavy HGV: b = 5.58, a = 12.8, Medium HGV: b = 5.58, a = 10.09, Light HGV: b = 5.58, a = 8.02 Hao, H., Wang, H., Ouyang, M., & Cheng, F. (2011). Vehicle survival patterns in China. Science China Technological Sciences, 54(3), 625-629.
weibull2: exp(-((time + b)/a)^b ), defaults b = 11, a = 26 Zachariadis, T., Samaras, Z., Zierock, K. H. (1995). Dynamic modeling of vehicle populations: an engineering approach for emissions calculations. Technological Forecasting and Social Change, 50(2), 135-149. Cited by Huo and Wang (2012)
if (FALSE) { # \dontrun{
vehLIA <- rep(1, 25)
PV_Minia <- age(x = vehLIA)
PV_Minib <- age(x = vehLIA, type = "weibull2", b = 11, a = 26)
PV_Minic <- age(x = vehLIA, type = "double_logistic", b = 21, a = 0.19)
PV_Minid <- age(x = vehLIA, type = "gompertz", b = -0.137, a = 1.798)
dff <- data.frame(PV_Minia, PV_Minib, PV_Minic, PV_Minid)
colplot(dff)
} # }